Emotional granularity describes the ability to create emotional experiences that are precise and context-specific. Despite growing evidence of a link between emotional granularity and mental health, the physiological correlates of granularity have been under-investigated. This study explored the relationship between granularity and cardiorespiratory physiological activity in everyday life, with particular reference to the role of respiratory sinus arrhythmia (RSA), an estimate of vagal influence on the heart often associated with positive mental and physical health outcomes. Participants completed a physiologically triggered experience sampling protocol including ambulatory recording of electrocardiogram (ECG), impedance cardiogram (ICG), movement, and posture. At each prompt, participants generated emotion labels to describe their current experience. In an end-of-day survey, participants elaborated on each prompt by rating the intensity of their experience on a standard set of emotion adjectives. Consistent with our hypotheses, individuals with higher granularity exhibited a larger number of distinct patterns of physiological activity during seated rest, and more situationally precise patterns of activity during emotional events: granularity was positively correlated with the number of clusters of cardiorespiratory physiological activity discovered in seated rest data, as well as with the performance of classifiers trained on event-related changes in physiological activity. Granularity was also positively associated with RSA during seated rest periods, although this relationship did not reach significance in this sample. These findings are consistent with constructionist accounts of emotion that propose concepts as a key mechanism underlying individual differences in emotional experience, physiological regulation, and physical health.

Predictive processing is emerging as a common neurocomputational hypothesis to account for diverse psychological functions subserved by a brain, enabling characterization of computational capacities of its distinct substructures. However, the literature is currently lacking a systems-level framework for understanding brain structure-function relationships based on the unified computational principles. Here, we contribute to this framework by examining gradients of functional connectivity as a low dimensional spatial representation of functional variation within a given structure. Specifically, we investigated functional connectivity gradients in the cerebral cortex, the cerebellum, and the hippocampus using resting-state functional MRI data collected from large samples of healthy young adults. We then evaluated the degree to which these structures share common principles of functional organization by assessing the correspondence of their gradients. We show that the organizing principles of these structures primarily follow two functional gradients consistent with the existing accounts of predictive processing: A model-error gradient that describes the flow of prediction and prediction error signals, and a model-precision gradient that differentiates regions involved in the representation and attentional modulation of such signals in the cerebral cortex. Using these gradients, we also demonstrate triangulation of functional connectivity involving distinct subregions of the three structures, suggesting the existence of parallel functional circuits that may subserve different aspects of predictive processing in the brain. These findings allow formulation of novel computational hypotheses about the functional relationships between the cerebral cortex, the cerebellum, and the hippocampus that may be instrumental for understanding the brain's dynamics within its large-scale predictive architecture.

Gradient mapping is an important technique to summarize high dimensional biological features as low dimensional manifold representations in exploring brain structure-function relationships at various levels of the cerebral cortex. While recent studies have characterized the major gradients of functional connectivity in several brain structures using this technique, very few have systematically examined the correspondence of such gradients across structures under a common systems-level framework. Using resting-state functional magnetic resonance imaging, here we show that the organizing principles of the isocortex, and those of the cerebellum and hippocampus in relation to the isocortex, can be described using two common functional gradients. We suggest that the similarity in functional connectivity gradients across these structures can be meaningfully interpreted within a common computational framework based on the principles of predictive processing. The present results, and the specific hypotheses that they suggest, represent an important step toward an integrative account of brain function.

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